Questions tagged [sensitivity-analysis]

Auxiliary methods intended to check if the outcome of an analysis strongly depends on the model assumptions, preprocessing steps, presence of outliers, etc.

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59 views

What does it mean if the sobol main and total effects indices are the same?

What does it mean when the total and main effects ANOVA indices are the same? Does it mean there is zero interaction of the different inputs? Is there some other way to quantify or understand that? I ...
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Matching: strange p-value for Rosenbaum sensitivity test using rbounds package

I'm using R to conduct genetic matching (Matching package) and do Rosenbaum sensitivity tests to test the sensitivity of my ...
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Bounding the sensitivity of a posterior mean to changes in a single data point

There is a real-valued random variable $\theta$ with a given distribution. Define the data points $$X_i = \theta + \epsilon_i \; \text{for } i\in\{1,\ldots,n\},$$ where $\epsilon_i$ are identically ...
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How to calculate sobol indices?

I have question regarding how exactly one should calculate the Sobol's indices. Specifically the V_i. I have 3 different paramters and 3 different values for each of them so 27 combinations in total. ...
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sensitivity analysis without model

Perhaps I should call it input-output correlation test, and the tricky part is no model exists between those inputs and outputs. The basic idea is there are many inputs and one output, and I would ...
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Individual significance of data points in correlation

In this question on stackoverflow, I asked about how it is possible to find the individual significance of each correlation coefficient of each node. I answered the question myself later stating that ...
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Sensitivity rates where TP =0 and FN =0

In calculating sensitivity rates where TP =0 and FN =0 and the formula is TP/(TP+FN) - although it mathematically won't compute, does this equate to 100% sensitivity since it has correctly identified ...
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How to interpret sensitivity index?

I am trying to do risk analysis on the statistical models we created. I used the sobol sensitivity analysis method to calculate the sensitivity indexes, using a python package SALib. Now how do I ...
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Sensitivity Analysis with categorical predictive variables in R

I am doing a project where I have to predict the Sales Units in fashion and intend to run a Random Forest, Neural Networks and Support Vector Machine models. However, my predictive variables are all ...
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Which approach should be used to compare two different measurement techniques of same samples?

I have individually measured failure forces of 8 materials and those recorded with A method and B method in same time: 8 results in each method, A=8 and B=8. The range of data of both measurement ...
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Understanding Conditional Variance Decomposition for Sensitivity Analysis (Sobol Indices)

I am trying to understand both terms of a formula that describe the variance of a model output, Y, in terms of its conditional variance, as below: $E_{X_i}\left(V_{X_{--i}}\left(Y\middle| X_i\right)\...
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What is the difference between sensitivity analysis and correlation analysis?

How do you compare them with each other in the statistical context? I have a set of inputs and outputs for which I build a random forest model. Can I use the model to perform sensitivity analysis ...
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Sensitivity of regression parameters to noise

How sensitive are the parameters obtained from OLS, logistic or other regression methods to noise ? By noise, I mean minor changes. For e.g. adding a small noise $-1<\Delta<1$ to $\beta_1$ in $...
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Analyse sensitivity of hyper-parameters of Machine Learning Models

I want to analyse how sensitive my non neural net machine learning models are to the choice of the different parameters. I am currently using grid search to tune the models. Is there any method that I ...
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Global sensitivity Morris method - choice of delta and normalisation of the elementary effects

I have few questions regarding the Morris method (as decribed e.g. in Campolongo, Cariboni, Saltelli, Environmental Modelling & Software 22, 2007 or Wenthworth et al. J. Uncertainty Quantification ...
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Different result in sensitivity analysis

the result of the sensitivity analysis when changing the below equation from using market value to book value as the measurement of leverage, Leverage = β0 + β1 PROF + β2 SIZE + β3 TANG + β4GROWTH +...
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comparing sensitivities

We want to compare two sampling methods. All patients will go through a DNA test for diagnosis(positive or negative). Then samples are taken to identify the determine the type of bacteria and ...
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Sensitivity index from Spearman's Rank-Order Correlation Coefficient

I calculated the Spearman's Rank-Order Correlation Coefficients for all free variables (over 200) in a model. I have about 5000 samples, and get comfortably small p values. I was wondering whether I ...
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Latin hypercube sampling with categorical variables

I would like to run sensitivity analyses on my agent-based model. My model has 20 parameters that I need to vary. I generated 8 artificial landscapes that vary in resource aggregation (r) and my model ...
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Calculate the contribution of features within a sentiment analyses

I did a sentiment analysis using the sentiwordnet lexicon. The sentiment analyses was done on Tweets, which were annotated as positive, negative or neutral. Now I want to know which features in the ...
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Fitting multiple polynomial regression

I hope someone could advise to interpret and report outputs of the multiple polynomial regression fit. I am trying to do a simple sensitivity analysis of an empirical threshold-based ecological model ...
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Sensitivity analysis of transition probabilities in a Markov chain

Does anyone know of a method of sensitivity analysis for investigating the effect of perturbing transition probabilities $p_{ij}$ from a Markov transition matrix? I have a series of n=400 sequences ...
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Sensitivity and specificity on cases rather than individual days

I have data collected from sensors to detect disease in several individuals. Each individual has a known disease status (labelled Actual in the table below) for ...
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Evaluating Impact of Unobserved Confounders - Is the E-Value applicable for Non-Significant Group Differences?

I have conducted an analysis of treatment effects based on observational data (via statistical matching). As suggested by VanderWeele and Ding (2017), I want to evaluate the sensitivity of my analysis ...
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Is there an accepted way to interpret d' (d-prime) for evidence of detection

I have run a learning experiment, with a yes-no familiarity test at the end, and computed d' across various conditions. Is there some rule of thumb (perhaps dependent on sample size) as to how d' ...
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What is the correct parameter range to choose when conducting a sensitivity analysis?

When conducting a (variance-based) sensitivity analysis, should I set the range of a specific parameter to its maximum allowable range, or restrict it to something more appropriate for my specific ...
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When is it justifiable to ignore explanatory variable endogeneity in a regression model?

I have three related questions: Is there a way to conduct a back of the envelop calculation that informs the reader on the degree of endogeneity we should have to bias the OLS estimates in a ...
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Causal Mediation Analysis Sensitivity Analysis

As part of a research project we have to perform causal mediation analysis(CMA) on R. Since mediation package is kind of limited for our research purposes, we ...
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Sensitivity Measures for GEE Model

Is there any method (e.g. like Cooks D) implemented in R to identify leverage points for GEE Models? I used geepack to fit my models and would like to do a sensitivity analysis now. However, I don't ...
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Unstable feature importance and optimized mtry values in Random Forest

I am working with a dataset of 17 predictors and 1000 observations. I am trying to find the most important variables, for which I am using the permutation-based OOB-MSE. My problem is that each time ...
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Sobol method vs OAT approach

I have used one at a time (OAT) approach in my model for sensitivity analysis. Which gives me elementry effects and variances for the input parameters, helping me to eliminate the less influencial ...
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How to analyses sensitivity for understanding which variables are the most effect on the predictive model

I have a dataset with 150 observations. The dataset has 9 input parameters and 1 output parameter. I have built a predictive model (Random Forest) using the dataset. And now, I want to know that which ...
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How to carry out a 5 input 5 output sensitivity analysis?

I have a vehicle (truck) model. There are five inputs, which are: Turn Radius Longitudinal slope (going up or down= Cross slope of the road Road friction Cargo weight By varying each of these inputs ...
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318 views

Difference between Sensitivity analysis and Design of Experiments

Reading on wikipedia about the methods for sensitivity analysis: different methods are stated. At the end of the wikipedia page, a section called Related Concepts speaks about Design of experiments (...
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Sobol Sensitivity Analysis

I want to use Sobol SA with Sobol sampling to find the most influential parameters on the energy consumption of a pilot building. I have 40 input variables (building characteristics) that some have ...
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Unexpected Negative Partial Derivatives for Input Features to Neural Network

Using a relatively standard MLP neural network, I model the total duration of an activity based on many counts of sub-activities, where the relationships between the sub-activities and duration may be ...
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Understanding Sobol in R Package Sensitivity

Sobol method quantifies the contributions of input variance to output variance. For example, given a model with two inputs and one output, one might find that 70% of the output variance is caused by ...
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one way sensitivity analysis with categorical variables

I am studying the impact of different factors on the expected price change for drugs. I have a regression model that looks like the following: Y=a+B1x1+B2x2... I would like to conduct some one-way ...
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Prior sensitivity analysis: The case of a Beta prior

I had a question regarding how to best perform "sensitivity analysis" on a Beta prior used for a estimating a proportion from a binomially distributed data problem (i.e., beta-binomial problem). I ...
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What is the correct method for comparing sensitivity and specifity of different tests?

What is the correct method of comparing efficiency of different test for one sample of individuals? Are ROC and AUC enough? Comparing of sensitivity and specifity values of the tests with McNemar ...
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How to evaluate sensitivity and specifity of different tests

I am comparing two test using different cut-off values with golden standart test . How is it to be reported. Is it sufficent to report sensitivity, specivity, PPV and NPV with 95 CI or should I ...
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How to choose a ML model when the goal is both reasonable prediction AND inference?

I'm relatively new to machine learning. I have come across the "ML is for prediction not inference” statement but this did not really sink in until my current project, a marketing mix modelling ...
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Linear Regression: How to favour less “sensitive” parameters?

I have a simple regression model (y = param1*x1 + param2*x2). When I fit the model to my data, I find two good solutions: Solution A, params=(2,7), is best on the training set with RMSE=2.5 BUT! ...
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Categorical data in sensitivity analyses

I have a model that will evaluate the carbon footprint of a product (i.e. tally the greenhouse gas emissions that were generated in each of the activities required to make, use and dispose of the ...
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Uncertainty and sensitivity analysis on modelling problems [closed]

I am starting to write the literature review for a project and my supervisor wants me to do an Uncertainty analysis and sensitivity analysis for a Modelica model he has. I am new to both concepts, do ...
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How many sobol sequences should I generate for sensitivity analysis?

I have a benchmark application that has 10 variables of different ranges. The application has one output a value measuring the performance of the application. I want to determine which of the ...
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86 views

Implementation of the weighted sum model without knowing the weights

I am looking to combine n metrics to obtain 1 single unified metric. For example, let's say I have 2 metrics n1 and n2 for k elements. I am particularly interested in the one or two elements that have ...
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481 views

sensitivity analysis when only input and output values are available in R

I was asked to evaluate how the output changes in response to input variables. For this I have randomly create sensible value of input variables. These are then imported into a software which yields ...
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Sensitivity analysis of election poll correlation

I am interested in why the 2016 presidential election polls did a poor job in forecasting the result. One hypothesis I heard is that many of the polls conducted before the election were correlated, ...
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Sensitivity and Specificity calculations

My confusion matrix is as shown below ...